ArticleSensors (Basel, Switzerland)2021
A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.GeroScience · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Late Fusion Model for Emotion Recognition from Facial Expressions and Biosignals in a Dataset of Children with Autism Spectrum Disorder.Sensors (Basel, Switzerland) · 2025Article
- Nociceptive responses to short tetanic stimulation during posttetanic count monitoring in surgical patients: a retrospective cohort study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Article
- Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification.Scientific reports · 2025Article
- Music emotion recognition based on temporal convolutional attention network using EEG.Frontiers in human neuroscience · 2024Article
- A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.Frontiers in neuroscience · 2023Article
- A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning.Computational intelligence and neuroscience · 2023Article
- Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements.Sensors (Basel, Switzerland) · 2022Article
- EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features.Sensors (Basel, Switzerland) · 2022Article
- Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.Sensors (Basel, Switzerland) · 2022Article
- Automated Feature Extraction on AsMap for Emotion Classification Using EEG.Sensors (Basel, Switzerland) · 2022Article
- Article
- Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method.Frontiers in psychology · 2022Article
- Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.Frontiers in systems neuroscience · 2021Review
- Review on Emotion Recognition Based on Electroencephalography.Frontiers in computational neuroscience · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.